Indoor User Research Articles

Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment

Recently, artificial intelligence (AI) has been applied in various industries. One such application is indoor user positioning using Big Data. The traditional method for positioning is the global positioning system (GPS). However, the performance of GPS is limited indoors due to propagation loss. Hence, radio frequency (RF)-based communication methods such as WiFi and Bluetooth have been proposed as indoor positioning solutions. However, positioning performance inaccuracies arise due to signal interference caused by RF band saturation. Therefore, this study proposes indoor user positioning based on visible light communication (VLC). The proposed method involves the sequential application of fingerprinting and double deep Q-Network. Fingerprinting is utilized to define the action and state of the double deep Q-Network agent. The agent is designed to learn and locate the reference point (RP) closest to the user’s position in a shorter search time. The core idea of the proposed system is to converge a Cell-ID scheme and fingerprinting. Through this, the initial state of the double deep Q-Network agent can be limited. A limited initial state can increase the positioning speed. Simulation results show that the proposed scheme attains a positioning resolution of less than 13 cm and achieves a processing time of less than 0.03 s to obtain the final position in VLC-based office environments.

Enhancing Indoor User Localization: An Adaptive Bayesian Approach for Multi-Floor Environments

Enhancing Indoor User Localization: An Adaptive Bayesian Approach for Multi-Floor Environments

RSSI 데이터 기반 머신러닝 실내 측위 연구

With the recent development of science and technology, localization technology has made it possible to provide customized mobile services based on real-time location information to users through smartphones and Internet of Things devices. In particular, GPS-based real-time localization is commonly used. However, unlike outdoors, not only does the GPS reception rate decrease significantly indoors, but it is also difficult to accurately measure the user's indoor location using only GPS latitude and longitude information. In this study, we propose to measure the user's indoor location using RSSI (received signal strength indicator). For this purpose, an indoor localization study was conducted by applying machine learning classifiers, SVM, decision tree, ExtraTrees, random forest, and KNN to RSSI data. Among the machine learning classifiers, random forest exhibited the best performance. Therefore, we applied random forest-based RFE to extract features from RSSI data. We confirmed that even with a smaller amount of data, it was possible to achieve more accurate indoor positioning. In addition, it was confirmed through regression analysis that latitude and longitude can be estimated from RSSI data, so that indoor as well as outdoor locations can be estimated.

How occupant positioning systems can be applied to help historic residences manage energy consumption: A case study in China

Historic residences often suffer from low energy efficiency due to inadequate management and obsolete infrastructure. To ensure these buildings operate in an environmentally friendly manner, intelligent energy control and management are necessary. Accurate indoor user positioning is fundamental to realizing occupant-centric controls. However, historic dwellings, with architectural heritage preservation requirements and diverse operational modes, present multiple challenges in terms of occupant monitoring and energy management. This research proposes a BLE positioning system based on the SAE-CNN algorithm to realize the analysis and prediction of occupant locations in a cost-effective and minimally-intrusive manner. The process begins by dividing spaces based on the mapped BIM model and cluster analysis. The RSSI maps of wireless signals collected from the Bluetooth beacons in each space are then preprocessed by Gaussian filter and sliding window techniques. Lastly, a machine learning algorithm refines the prediction model, which is integrated into a digital twin platform designed for energy consumption management. The system was implemented in a traditional Chinese dwelling in Wufu town, Fujian Province. The results are promising, showing that the positioning accuracy reached 1.5–2 m (95 % confidence interval), while the platform effectively presented the real-time positioning results as well as realized occupant-centric energy management. Moreover, the collected location information shed light on occupants' spatial usage habits, offering valuable insights for devising decarbonization retrofit strategies.

Some Design Considerations in Passive Indoor Positioning Systems.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern Analysis

The recent advances in smart building technologies have enabled us to collect massive Wi-Fi network based trajectory data, which provide an unparalleled opportunity for understanding the indoor user mobility pattern and enabling a wide range of business applications. While some previous studies have explored the Wi-Fi positioning of users, there still lacks a systematic and effective solution for indoor user mobility pattern analysis based on Wi-Fi trajectory data. To this end, in this paper, we propose a unified framework for modeling Wi-Fi trajectory data, namely HWTE, which can empower various tasks of indoor user mobility pattern analysis, such as user classification, next location prediction and schedule estimation. Specifically, we first propose a session trajectory construction module to extract the spatio-temporal semantic information from the Wi-Fi trajectories of users. Then, we devise a pre-training module to learn the unified representation of Wi-Fi trajectories. In particular, a session position embedding technique and a position query task is introduced to enhance the representation ability of the whole trajectory. Moreover, we further propose a hierarchical Transformer-based fine-tuning module to support various application tasks with time and space efficiency. Finally, we validate our framework on a real-world dataset with all three kinds of downstream tasks.

Quad Element Beamforming Microstrip Antenna for Indoor User Equipment Localization at 6 GHz Band

Quad Element Beamforming Microstrip Antenna for Indoor User Equipment Localization at 6 GHz Band

Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones

Nowadays, smartphones have become indispensable in people’s daily work and life. Since various sensors and communication chips have been integrated into smartphones, it has become feasible to provide indoor positioning using phones. This paper proposes such a solution based on a smartphone, combining Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR) in the particle filter framework to realize real-time and reliable indoor positioning. First, the smartphone’s built-in accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible method for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. However, a single positioning technology has limitations, e.g., the cumulative error of PDR and the received signal strength fluctuation of BLE. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experiments are conducted on a floor in a campus building. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is reduced by 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has a vast application prospect in indoor navigation, indoor user tracking, and interactive experience for indoor visitors.

A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System

A receive signal strength (RSS) fingerprinting-based indoor wireless localization system (I-WLS) uses a localization machine learning (ML) algorithm to estimate the location of an indoor user using RSS measurements as the position-dependent signal parameter (PDSP). There are two stages in the system's localization process: the offline phase and the online phase. The offline phase starts with the collection and generation of RSS measurement vectors from radio frequency (RF) signals received at fixed reference locations, followed by the construction of an RSS radio map. In the online phase, the instantaneous location of an indoor user is found by searching the RSS-based radio map for a reference location whose RSS measurement vector corresponds to the user's instantaneously acquired RSS measurements. The performance of the system depends on a number of factors that are present in both the online and offline stages of the localization process. This survey identifies these factors and examines how they impact the overall performance of the 2-dimensional (2-D) RSS fingerprinting-based I-WLS. The effects of these factors are discussed, as well as previous researchers' suggestions for minimizing or mitigating them and future research trends in RSS fingerprinting-based I-WLS.

Exploiting Environmental Information Using HsMMs for Smartphone User Tracking

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users (MPUs) in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal-processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference for staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This article proposes an online HsMM forward recursion (HsMM-FR) algorithm to incorporate this information for real-time smartphone user tracking. We apply the proposed HsMM-FR algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.

Performance analysis of flexible indoor and outdoor user distribution in urban multi-tier heterogeneous network

The next generation heterogeneous networks (hetnet) refer to a complex eco system of radio cells with varying coverage ranges and technologies that interoperate to deliver the required capacity and coverage. This paper addresses the impact of large scale transmissions and analyses the performance of a typical user distribution in an urban multi-tier hetnet. A Boolean scheme of building blockage has been used to abstract the urban environment, which affects the architecture of hetnet with millimetre wave (mmW) access. A distribution mechanism is proposed to enable the indoor users to operate in mmW band whereas the outdoor users to operate in microwave (μWave) band. An indoor user can be connected with mmW band if the line-of-sight (LOS) link exists. Otherwise, the exploitation is provided with μWave band to enhance the coverage. By employing tools from stochastic geometry, the mathematical expressions are derived for coverage probability, rate and spatial average channel capacity of a user in indoor and outdoor environments. The exact mathematical results show that an impact of small cell occupation ratio results in maximised spatial average channel capacity of an outdoor hetnet. The significant enhancement is provided with mmW small cell in terms of rate and coverage.

Feasibility study of the UXindoor framework in two public office buildings: A standardized approach to scoring indoor user experience with qualitative and quantitative techniques

A human-centered strategy is crucial for effective energy retrofits, as the subjective experience of occupants directly affects the energy performance of buildings. To address this, we introduce UXindoor, a standardized scoring framework for assessing the User eXperience (UX) of Indoor Environmental Quality (IEQ). UXindoor is a key component of the PRIME Energy-Indoor project, which aims to guide integrated building energy retrofits based on real energy usage and IEQ data. The framework utilizes questionnaires, handheld instruments, and IoT environmental sensors in a unitary assessment framework. This feasibility study presents the results of the pilot-testing of the framework in two public buildings in Thessaloniki, Greece over a six-month period from September 2022 to February 2023. The study discusses the advantages of the proposed system as well as its limitations.

Device-based Real-time Single User Indoor Localization Using Internet of Things

Background: Localization is an important area of implementation of the internet of things based on Wireless Sensor Networks. Outdoor user tracking is possible using the global positioning system; however, the global positioning system accuracy decreases in indoor environments. To overcome this problem, the wireless sensor network is used in internet of things-based technology for localization. Objective: The wireless sensor network-based indoor localization is categorized into two categories; rangebased and range-free localization. In range-based localization, it first computes the relative distance and then calculates relative coordinates. In range-based techniques, distance and position are calculated. The internet of things-based localization uses the range-based and range-free techniques of a wireless sensor network to localize any object. The Light Dependent Resistor-based localization work has been proposed in previous research. In this research, the light-dependent resistor traces a person’s entry /exit event as the person switches ON/OFF lights of the building. However, it was not a sufficient effort to localize a person using light dependent resistor. To overcome the problem of light dependent resistor, the two PIR sensors have been used in each room along with one RFID based approach in this study. Methods: An indoor scenario has been considered in this study. The hardware setup has been configured to trace the user. When a user enters inside a building, he will switch on the lights, and the light sensor records the light intensity and gives some reading. The difference in the reading of the light sensor (before switching ON the light and after switching OFF the light) gives some clue about a user in an indoor scenario. Nevertheless, if the lights of many rooms remain switched ON, then the user cannot be localized using the above method. In order to sort out this ambiguity of light sensors, two passive infrared sensors in each room along with one radio frequency identification-based model have been proposed in the present study. Implementing the single-user localization using a light-dependent resistor sensor becomes erroneous if a person moves from one room to another and the lights get turned ON/OFF. Moreover, the LDR-based model is affected by sunlight during the daytime. Results: As the implementation of passive infrared sensors along with one RFID-based localization technique gives 93% efficiency, the light-dependent resistor-based localization system gives an efficiency of 35%. Conclusion: The light-dependent resistor-based approach is prone to more errors because the user may enter multiple rooms while the lights of each room remain ON. To overcome this problem, a passive infrared sensor and radio frequency identification-based approach for a single user indoor localization has been proposed. The proposed techniques are easy and cost-effective for implementation. The results show that the proposed technique provides better localization accuracy than a light-dependent sensor-based technique for single-user indoor localization.

In-Home Cannabis Smoking Among a Cannabis-Using Convenience Sample from the Global Drug Survey: With Weighted Estimates for U.S. Respondents.

Introduction: Secondhand and thirdhand tobacco smoke exposure most often occur at home, but little is known about occurrences of in-home cannabis smoking. We ascertained in-home cannabis smoking reported by all cannabis-using (i.e., used in the last 12 months) respondents to the Global Drug Survey (GDS; international-GDS sample), and among U.S. cannabis-using respondents (US-GDS sample). Materials and Methods: We used data collected November 2019-January 2020 for the 2020 GDS, an annual anonymous, cross-sectional survey; respondents were 16+ years old, from 191 countries. We estimated any and daily in-home cannabis smoking in the last 30 days among international-GDS respondents (n=63,797), using mixed effects logistic regression. US-GDS respondents (n=6,580) were weighted to the covariate distribution of the nationally representative 2018 National Survey on Drug Use and Health cannabis-using sample, using inverse odds probability weighting, to make estimates more generalizable to the U.S. cannabis-using population. Results: For the international-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 63.9% of men, 61.9% of women, and 68.6% of nonbinary people; and by age (<25 years old=62.7%, 25-34 years old=65.0%, and 35+ years old=62.8%). Daily in-home cannabis smoking was highest among nonbinary (28.7%) and respondents 35+ years of age (28.0%). For the weighted US-GDS cannabis-using respondents, any in-home cannabis smoking was reported by 49.8% of males and 61.2% of females; and by age (<25 years old=62.6%, 25-34 years old=41.8%, 35+ years old=57.9%). Weighted daily in-home smoking was 23.2% among males and 37.1% among females; by age (<25 years old=34.8%, 25-34 years old=27.8%, and 35+ years old=21.6%). Conclusions: There was high daily cannabis smoking in homes of international-GDS and US-GDS respondents who used cannabis in the last 12 months. In part, due to cannabis legalization, the number of users worldwide has increased over the past decade. Criminal stigma historically associated with cannabis continues to drive those users indoors. In this context, our findings support further investigation of cannabis use behavior to understand how often people are exposed to secondhand and thirdhand cannabis smoke and the consequences of that exposure.

EA-CNN: A smart indoor 3D positioning scheme based on Wi-Fi fingerprinting and deep learning

Accurate indoor location information in multi-building/floor environments is essential for establishing many indoor location-based services (LBS). Wi-Fi fingerprinting with received signal strength indicators (RSSI) has become one of the most practical techniques to localize users indoors. However, the fluctuation of wireless signals caused by fading, the multipath effect, and device heterogeneity leads to considerable variations in RSSIs, which poses a challenge to accurate localization. This paper proposes an indoor positioning method, which is constructed based on a convolutional neural network (CNN) framework. Specifically, a novel model by combining an extreme learning machine autoencoder (ELM-AE) with a two-dimensional CNN is proposed. The ELM-AE extracts critical features by reducing input dimensions, while the CNN is trained to effectively achieve significant performance in the positioning phase. To increase positioning accuracy and deal with data shortages, a data augmentation strategy by frequently adding noisy data to the original fingerprint map is devised. The statistical properties namely, the RSS values of each MAC address, are utilized to adjust input noise. The performance of the proposed system is evaluated on two Tampere and UJIIndoorLoc datasets. The experimental results show that EA-CNN achieves better performance than CNN by decreasing average positioning error up to 40.95% and 43.74% in Tampere and UJIIndoorLoc datasets, respectively. Compared to state-of-the-art deep learning-based methods, the positioning performance improves up to 68.36% in Tampere and 67.56% in the UJIIndoorLoc dataset by exploiting only 25% of the training samples.Compared to several state-of-the-art deep learning models, EA-CNN achieves higher accuracy in both positioning and floor estimation.

Reconfigurable Intelligent Surface aided Indoor and Outdoor User Distribution in Heterogeneous Network

Millimeter wave communication suffers from static blockages such as trees, buildings and so on. Reconfigurable Intelligent Surfaces (RISs) has been adapted to solve this blockage problem and enable the urban environment user to choose mmW enabled small cells as their source transmitter or to choose macro cells in case of non-line of sight exists. RIS is a promising network technology to improve the quality of service parameters such as spectral efficiency and energy efficiency by artificially reconfiguring the propagation environment of electromagnetic waves. An indoor user can be connected with mmW band if the line of sight (LOS) link exists. Otherwise, the system utilizes RIS transmission model to have reliable and low-latency communication. It reflects the capability of RISs to enable enhanced communications in challenging environments. An optimization problem is formulated to maximize the sum data rate of an indoor user by phase shift optimization at the RIS. The outage probability of the proposed scheme is analyzed under Rician fading channel. The proposed RIS enabled method targets to enhance the overall performance in terms of average spectral efficiency and achievable data rate in the presence of blockages and system imperfections. The data rate is increased by three fold times than that of the transmission without RIS. The utility of this framework is discussed for both indoor and outdoor environments.

Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system

Traditional control strategies in district heating system (DHS) are generally based on meteorological parameters and experience, which rarely consider user's energy-saving behaviors. In new heating mode that integrates heating metering and temperature control, the application of on-off temperature control valves (on-off TCV) makes it possible for users to save energy. This paper proposes a control strategy of DHS that integrates characteristics of user's energy-saving behaviors and realizes combined control of feedforward and feedback. Firstly, we identify user's heating usage based on set indoor temperature and TCV's closing time ratio, and divide regulation periods; secondly, the TCV's closing time ratio of system is used to predict heat load of different regulation periods under different heating modes; finally, the deviation between target indoor temperature and actual value is used to dynamically correct heat load, and then supply curve is obtained and applied. The results show that prediction accuracy is improved. Meanwhile, user's indoor temperature is closer to target value and the system realized energy-saving operation. The energy-saving rates are 4.6%, 2.63% and 19.83% respectively in early cold, alpine cold and late cold periods. Throughout heating season, the proportion of TCV's closing time has a little change and the hydraulic condition is stable.

Performance Analysis of flexible Indoor and Outdoor User distribution in Urban Multi-Tier Heterogeneous Network

Performance Analysis of flexible Indoor and Outdoor User distribution in Urban Multi-Tier Heterogeneous Network

Indoor Localization System Based on Mobile Access Point Model MAPM Using RSS With UWB-OFDM

Indoor tracking is one of the most attractive topics in communication and information technology. Indoor localization mechanisms can help people for navigating in complex environments. Those with complex structures such as airports, shopping malls, hospitals, and others have a massive population (Crowdsourcing) that blocks the visibility (NLOS) between the access points and the users. Accordingly, we present the mobile access point model MAPM as a new algorithm for positioning users indoors based on the mathematical model of received signal strength RSS using UWB-OFDM. MAPM participates in crowdsourcing indoors by using users as mobile access points (MAP). That may decrease the use of several static access points (SAP) indoors and increase the localization precision. First, we use ultra-wideband UWB with orthogonal frequency division multiplexing OFDM as a communication gadget between the users and the system. Those have good quality in terms of accuracy, time response, energy consumption, and efficiency against interference. Second, we measure the received signal strength then, we estimate the distance between users and the access points using the mathematical model of RSS. These can be more adapted to changing environments and device heterogeneity than RSSI fingerprints. Third, we calculate the position information by using the Euclidean distance formula. In this way, we can reduce the effect of most of the problems on the indoor positioning system. We simulate the proposed algorithm in a platform based on three tools, MATLAB for system information processing, MYSQL as a system database, and a control interface coded in Java. In the given simulation results, we find that the average MAPM error in the case of 30 users is 10.19 cm, and the detection rate has a value of 86.66%.

Coverage analysis of indoor users from mm‐wave multi‐RAT small cells deployed along roads

AbstractThe initial deployment of millimeter‐wave (mm‐wave) base stations (BSs), for example, by Verizon and AT&amp;T will be on top of lamp‐posts along the roads of an urban scenario. These deployments will mainly target the outdoor pedestrian users and provide on‐demand ultra‐high data rates. However, as the mm‐wave deployments mature, the operator must necessarily dimension the network by taking into consideration the requirements of the indoor cellular users as well. In this paper, we study the extent of indoor coverage with on‐road BS deployments, so as to enable the network operators with necessary system design and network dimensioning insights. We derive the tier and radio access technique (RAT) selection probabilities of the typical indoor user, and consequently, we characterize its signal to interference and noise ratio (SINR) coverage probability. Based on the density of roads in the city, we prescribe the optimal density of the BSs required to maximize the SINR coverage of the indoor users.